Locational Carbon Footprint of the Power Industry: Implications for Operations, Planning and Policy Making

  • Aleksandr Rudkevich
  • Pablo A. Ruiz
Part of the Energy Systems book series (ENERGY)


Jurisdictions across the globe are implementing CO2 emissions reduction policies. These policies typically ignore most locational issues, probably because the consequences of greenhouse gas emissions do not depend on the exact emission location. However, the response to emission policies and the costs and effectiveness of emissions reduction in power systems are time-varying and locational in nature. The first part of the paper elaborates on the economic properties of the concept of locational marginal carbon intensity and formulates an allocation of the carbon footprint of the electrical grid to individual generating units, transmission facilities and end users on a real time basis. In the second part, the theory of the marginal carbon footprint is applied to the derivation of the optimal investment policy underlying Renewable Portfolio Standards (RPS). The argument is made that the existing RPS policies are at best sub-optimal in their goal to reduce emissions of Carbon Dioxide and other greenhouse gases. A proposed optimal investment rule could serve to improve the efficiency of RPS policies.


Electrical grid Transmission congestion Marginal carbon intensity Renewable portfolio standard Carbon footprint 


  1. 1.
    The Regional Greenhouse Gas Initiative Inc (2005) Regional greenhouse gas initiative memorandum of understanding, New York.
  2. 2.
    Schwarzenegger A (2008) Exec. Order S-14-08, Office of the Governor of the State of California, Sacramento [Online].
  3. 3.
    Midwest ISO (2012) Multi value projects portfolio results and analyses, Indiana.
  4. 4.
    Shavel I (2009) Testimony before the Federal Energy Regulatory Commission on behalf of ITC Green Power Express, LLC. FERC, Washington, Docket ER09-681-000Google Scholar
  5. 5.
    Spiegel RJ, Greenberg DL, Kern EC, House DE (2000) Emissions reduction data for grid-connected photovoltaic power systems. Solar Energy 68(5):475–485CrossRefGoogle Scholar
  6. 6.
    Bettle R, Pout C, Hitchin E (2006) Interactions between electricity-saving measures and carbon emissions from power generation in England and Wales. Energy Policy 34(18):3434–3446CrossRefGoogle Scholar
  7. 7.
    Grubb M, Neuhoff K (2006) Allocation and competitiveness in the EU emissions trading scheme: policy overview. Clim Policy 6(1):7–30Google Scholar
  8. 8.
    Chen Y, Sijm J, Hobbs BF, Lise W (2008) Implications of CO [Trial mode] emissions trading for short-run electricity market outcomes in northwest Europe. J Regul Econ 34(3):251–281CrossRefGoogle Scholar
  9. 9.
    Ruiz P, Rudkevich A (2010) Analysis of marginal carbon intensities in constrained power networks. In: Proceedings of the 43rd Hawaii international conference systems science, KoloaGoogle Scholar
  10. 10.
    Rudkevich A (2010) Locational carbon footprint and renewable portfolio standards. In: Proceedings of the 7th conference economics energy markets, Toulouse [Online].
  11. 11.
    Holland SP, Mansur ET (2006) The short-run effects of time-varying prices in competitive electricity markets. Energy J 27(4):127–155Google Scholar
  12. 12.
    The Carbon Trust (2007) Carbon footprinting: an introduction for organizations. The Carbon Trust, Witney, Publication CTV033 [Online].
  13. 13.
    Scientific Applications International Corporation (SAIC) (2006) Life cycle assessment: principles and practices. U.S. Environmental Protection Agency, Washington, EPA 600/R06/060Google Scholar
  14. 14.
    Clean Development Mechanism (2009) Tool to calculate the emission factor for an electricity system, UNFCCC, EB 50 Report, Annex 14.
  15. 15.
    Schweppe FC, Caramanis MC, Tabors RD, Bohn RE (1987) Spot pricing of electricity. Kluwer, NorwellGoogle Scholar
  16. 16.
    U.S. Department of Energy (2009) States with renewable portfolio standards, Washington.
  17. 17.
    United States Congress (2009) American Clean Energy and Security Act. H. R. 2454, 111th United States Congress, WashingtonGoogle Scholar
  18. 18.
    Lindstrom P (2008) Emissions of greenhouse gases report. Energy Information Administration, Washington, Report DOE/EIA-0573, Chap. 2 [Online].
  19. 19.
    Cheng X, Overbye TJ (2006) An energy reference bus independent LMP decomposition algorithm. IEEE Trans Power Syst 21(3):1041–1049CrossRefGoogle Scholar
  20. 20.
    Delson JK (1974) Controlled emission dispatch. IEEE Trans Power App Syst PAS-93:1359–1366CrossRefGoogle Scholar
  21. 21.
    Talaq JH, El-Hawary F, El-Hawary ME (1994) A summary of environmental/economic dispatch algorithms. IEEE Trans Power Syst 9(3):1508–1516CrossRefGoogle Scholar
  22. 22.
    Bertsekas DP (1999) Nonlinear programming, 2nd edn. Athena Scientific, BelmontMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Newton Energy GroupCalgarycanada
  2. 2.Charles River Associates, Boston UniversityBostonUSA

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